1. Field of Invention
The embodiments described herein relates to methods and systems to detect and measure the retinal disruption/elevation of optical coherence tomography (OCT) data, as well as to present the detection and measurement results using 3D OCT data.
2. Background State of the Arts
Optical Coherence Tomography (OCT) has been an important modality for imaging eyes and facilitating ophthalmologists to diagnose and treat subjects with different eye diseases, retinal diseases in particular. The importance of OCT to the field of ophthalmology has also dramatically increased since Fourier Domain OCT (FD-OCT) became commercially available. FD-OCT has much higher scanning speed and higher resolution than the traditional Time Domain OCT (TD-OCT) technologies.
One of the major pathologic changes for retinal subjects is retinal layers disruption from their normal locations, especially around the Retinal Pigment Epithelium (RPE) and Photoreceptor Inner Segment/Outer Segment (PR-IS/OS) area. Quantitative measurements of such disruptions provide important information for ophthalmologists to diagnose and treat patients.
Previous methods using 3D OCT data follow the same scheme of first segmenting retinal layers, and then detecting the disruption (e.g. drusen) by comparing the segmented layers with expected referenced layers or with some layers which are elevated from the segmented layers by some constants. The referenced layers are often generated by fitting some smooth surfaces to the segmented layers, assuming the layers are not disrupted by any disease or pathology. In general, the presence of a disruption is determined by only comparing two 2D surfaces; this means the original 3D OCT data is not fully utilized after the layer segmentations have been performed. Such scheme has at least four major drawbacks. First, such detection methods are error prone because they are highly dependent on results of layer segmentations. If the 2D surface segmentation is not optimal, the disruption detection will be directly affected and likely produce inaccurate results. Second, to reduce noise effects associated with OCT data, layer segmentation often employs smoothing operation which can likely introduce the problem of scale. Excessive smoothing (such as the case with a large smoothing scale) will likely reduce details in desired features, while insufficient smoothing (with a small smoothing scale) will likely be inadequate to reduce noise effectively to generate optimal layer segmentations. Third, methods assuming constant elevations from segmented layers are less clinically meaningful because disruptions often occur locally with different and unpredictable sizes. Finally, a majority of existing methods only detect disruptions above the referenced layers, and any disruptions under the referenced layers are ignored. Since disruptions can occur above and below the reference layers of interest, it is important to devise a method to detect and measure disruptions in both scenarios.